A delimited data set, such as data stored in a spreadsheet or a comma-separated value file, can be visually depicted using data graphics. In a simple example, certain spreadsheet applications allow a delimited data set to be visually depicted as a bar graph or other graph. In these applications, a first field in a delimited data set, such as a column of a spreadsheet, is mapped to an x axis of the bar graph and a second field in the delimited data set (e.g., another column of the spreadsheet) is mapped to a y axis of the bar. Values of the first mapped field are displayed on the x axis and corresponding values of the second mapped field are displayed on they axis, thereby creating the bar graph.
Rather than creating a wholly new graphical depiction such as a bar graph, some prior solutions modify pre-existing graphics to correspond to data in a delimited data set, thereby using the modified graphics to visually depict the delimited data. These solutions include data visualization platforms, such as Lyra, that map data sets to the visual properties of graphical objects in a graphics file. For example, a pre-existing graphic, such as a map of the United States, may include graphical objects, such as depictions of individual states. A first column of a spreadsheet is mapped to these graphical objects (e.g., values in a “State” column being mapped to visual depictions of the states), and a second column of the spreadsheet is mapped to visual properties of these graphical objects (e.g., values in a “Poverty level” column being mapped to different color values). In this manner, the separate graphical objects (e.g., states on the map) play the same role as the x axis of a bar graph in the example above, and the color variations for different graphical objects play the same role as they axis of the bar graph.
These prior solutions for creating graphical depictions of data sets from pre-existing graphics present disadvantages. One disadvantage includes using shapes (e.g., rectangles, circles, etc.) as the basis for a mapping. In the example of the Lyra data visualization platform provided above, a user would be limited to mapping a given value of a data field to an entire graphical object, such as a visual depiction of an entire state. This type of mapping causes the mapped data value to affect the visual property for the entire shape. This limited granularity in the data-to-graphics mapping causes limited flexibility in how data sets can be visually depicted using pre-existing graphics.
Another disadvantage of these prior solutions includes reduced flexibility in manipulating a graphical depiction of data that has already been generated. For instance, some data visualization platforms may generate a graphical depiction of a data set, such as a U.S. map with different colored states, but may not allow a user to manipulate properties of the graphical depiction. If a user, after viewing the graphical depiction generated from the data-to-graphic mapping, wishes to adjust visual properties of certain graphical objects (e.g. the coloring of depictions of U.S. states), the user may be required to rebuild the entire graphical depiction from scratch (e.g., by creating a new mapping of color values to data field values) rather than manually adjusting visual properties of specific graphical objects. Furthermore, existing data visualization platforms may not allow a user to manipulate graphical objects that are not mapped to any data fields (e.g., depictions of U.S. states for which no poverty data is present in the data set).
Therefore, it is desirable to provide tools for graphically depicting data sets that provide greater flexibility in how users can manipulate a graphical depiction that has been generated using a mapping to a data set.